ABSTRACT

Robot Localization is an emerging area in recent research and applications. The determination of location or localization is the basic requirement for robots to move in their office environment. This proposed work aims to build a map from a sparse set of noisy observations, taken from known locations by multiple sensors and is validated experimentally in indoor office environment. A set of training data is collected from each environment and processed offline to produce a GP Model (Gaussian Process Model). The robot uses this model to localize while traversing each environment. The sensors are used to extract information about the robot’s environment. Because a mobile robot moves around, it will frequently encounter unforeseen environmental characteristics. The sensors have only a limited range, and so it must physically explore its environment to build a map. So, the robot must not only create a map but also it must do so while moving and localizing to explore the environment. In the robotics terminology, this is called the simultaneous localization and mapping (SLAM), and then changing the robot’s trajectory as informed by its sensors during robot motion is called the Obstacle avoidance. The proposed system is used for avoiding real time obstacle in smooth surface by using feature extraction.